{"blocks": [{"key": "5fdf0991", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "97188269", "text": "Given a raw behavioral dataset, the interviewer asks you to perform end-to-end analysis: clean and explore the data, build a statistical model to predict conversion, evaluate it, and suggest improvements.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2d3a6e14", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2e10f205", "text": "Walk through your exploratory data analysis steps on the spot. Choose and train an appropriate statistical or machine-learning model; justify feature selection and preprocessing choices. Report performance metrics, interpret coefficients/feature importances, and recommend ways to improve the model and the experiment.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3b0b57dd", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f466efd9", "text": "Discuss missing-value handling, train/validation split, baseline models, ROC/AUC or lift, and possible feature engineering iterations.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}